Multiview video summarization using video partitioning and clustering |
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| Affiliation: | 1. Research scholar, Department of Information Technology, Cochin University of Science and Technology, Kochi, Kerala, India, 682022;2. Professor, Department of Information Technology, Cochin University of Science and Technology, Kochi, Kerala, India, 682022;1. Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, China;2. School of Big Data and Software Engineering, Chongqing University, Chongqing, China;1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China;2. Center for OPTical IMagery Analysis and Learning, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China |
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| Abstract: | Multiview video summarization plays a crucial role in abstracting essential information form multiple videos of the same location and time. In this paper, we propose a new approach for the multiview summarization. The proposed approach uses the BIRCH clustering algorithm for the first time on the initial set of frames to get rid of the static and redundant. The work presents a new approach for shot boundary detection using frame similarity measures Jaccard and Dice. The algorithm performs effectively synchronized merging of keyframes from all camera-views to obtain the final summary. Extensive experimentation conducted on various datasets suggests that the proposed approach significantly outperforms most of the existing video summarization approaches. To state a few, a 1.5% improvement on video length reduction, 24.28% improvement in compression ratio, and 6.4% improvement in quality assessment ratio is observed on the lobby dataset. |
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| Keywords: | Video summarization Surveillance videos Multiview video Video partition Clustering |
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